Automatic Camera Calibration by Landmarks on Rigid Objects
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138666" target="_blank" >RIV/00216305:26230/20:PU138666 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/12345/" target="_blank" >https://www.fit.vut.cz/research/publication/12345/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00138-020-01125-x" target="_blank" >10.1007/s00138-020-01125-x</a>
Alternative languages
Result language
angličtina
Original language name
Automatic Camera Calibration by Landmarks on Rigid Objects
Original language description
This article presents a new method for automatic calibration of surveillance cameras. We are dealing with traffic surveillance and therefore the camera is calibrated by observing vehicles; however, other rigid objects can be used instead. The proposed method is using keypoints or landmarks automatically detected on the observed objects by a convolutional neural network. By using fine-grained recognition of the vehicles (calibration objects), and by knowing the 3D positions of the landmarks for the (very limited) set of known objects, the extracted keypoints are used for calibration of the camera, resulting in internal (focal length) and external (rotation, translation) parameters and scene scale of the surveillance camera. We collected a dataset in two parking lots and equipped it with a calibration ground truth by measuring multiple distances in the ground plane. This dataset seems to be more accurate than the existing comparable data (GT calibration error reduced from 4.62% to 0.99%). Also, the experiments show that our method overcomes the best existing alternative in terms of accuracy (error reduced from 6.56% to 4.03%) and our solution is also more flexible in terms of viewpoint change and other.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Vision and Applications
ISSN
0932-8092
e-ISSN
1432-1769
Volume of the periodical
32
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
2-15
UT code for WoS article
000575425400001
EID of the result in the Scopus database
2-s2.0-85091965520